Deep learning-based methods for segmentation and labelling of clay

dc.contributor.authorÖrtendahl, Theo
dc.contributor.departmentChalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE)sv
dc.contributor.departmentChalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE)en
dc.contributor.examinerDijkstra, Jelke
dc.contributor.supervisorCasarella, Angela
dc.date.accessioned2025-07-31T11:40:19Z
dc.date.issued2025
dc.date.submitted
dc.description.abstractRecent advancements in x-ray technology have enabled non destructive 3D sub-micron imaging of clay. In this work, a 3D tomography of kaolinite particles is analysed. Conventional segmentation algorithms are used along with a deep learning-aided method, which brings novelty to the clay research area. The clay image is segmented to acquire morphological properties of the material, intended to be used to inform continuum models for clay used at the engineering scale. Imaging clay is especially challenging because of its small particle size and thin aggregated platelets. A small clay dataset will be developed to evaluate the performance of segmentation techniques and to train a machine learning-model called the Segment Anything Model 2 (SAM 2). Advanced contemporary studies in biomedical segmentation show compelling results using SAM 2 and this study is proposing to bring this technique into the area of geomechanics. This study aims to lay a path for future research to strengthen the link between physical relationships and observed clay behaviour by providing information of clay micro-structures.
dc.identifier.coursecodeACEX60
dc.identifier.urihttp://hdl.handle.net/20.500.12380/310271
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectClay
dc.subjectSAM
dc.subjectSegment Anything Model 2
dc.subjectNano-XCT
dc.titleDeep learning-based methods for segmentation and labelling of clay
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeInfrastructure and environmental engineering (MPIEE), MSc

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